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Symbolic Artificial Intelligence
In expert system, symbolic synthetic intelligence (also called classical expert system or logic-based synthetic intelligence) [1] [2] is the term for the collection of all methods in artificial intelligence research that are based on top-level symbolic (human-readable) representations of problems, reasoning and search. [3] Symbolic AI utilized tools such as logic programs, production rules, semantic webs and frames, and it developed applications such as knowledge-based systems (in specific, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated preparation and scheduling systems. The Symbolic AI paradigm led to seminal concepts in search, symbolic programming languages, representatives, multi-agent systems, the semantic web, and the strengths and restrictions of official knowledge and thinking systems.
Symbolic AI was the dominant paradigm of AI research study from the mid-1950s until the mid-1990s. [4] Researchers in the 1960s and the 1970s were encouraged that symbolic techniques would ultimately succeed in producing a machine with artificial general intelligence and considered this the ultimate objective of their field. [citation needed] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, led to unrealistic expectations and guarantees and was followed by the first AI Winter as funding dried up. [5] [6] A second boom (1969-1986) happened with the increase of expert systems, their promise of catching business competence, and an enthusiastic business embrace. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed again by later frustration. [8] Problems with problems in understanding acquisition, keeping big knowledge bases, and brittleness in managing out-of-domain problems . Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists concentrated on dealing with underlying problems in dealing with unpredictability and in understanding acquisition. [10] Uncertainty was addressed with formal methods such as covert Markov models, Bayesian thinking, and analytical relational knowing. [11] [12] Symbolic maker finding out resolved the understanding acquisition issue with contributions consisting of Version Space, Valiant’s PAC learning, Quinlan’s ID3 decision-tree knowing, case-based knowing, and inductive logic programs to learn relations. [13]
Neural networks, a subsymbolic method, had actually been pursued from early days and reemerged strongly in 2012. Early examples are Rosenblatt’s perceptron learning work, the backpropagation work of Rumelhart, Hinton and Williams, [14] and work in convolutional neural networks by LeCun et al. in 1989. [15] However, neural networks were not seen as successful up until about 2012: „Until Big Data ended up being commonplace, the general consensus in the Al community was that the so-called neural-network method was helpless. Systems simply didn’t work that well, compared to other approaches. … A transformation can be found in 2012, when a variety of people, consisting of a group of scientists dealing with Hinton, exercised a method to utilize the power of GPUs to enormously increase the power of neural networks.“ [16] Over the next a number of years, deep learning had incredible success in dealing with vision, speech acknowledgment, speech synthesis, image generation, and device translation. However, because 2020, as inherent troubles with bias, explanation, comprehensibility, and robustness ended up being more evident with deep knowing methods; an increasing variety of AI researchers have required integrating the best of both the symbolic and neural network approaches [17] [18] and resolving locations that both methods have problem with, such as sensible thinking. [16]
A short history of symbolic AI to today day follows listed below. Time durations and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture [19] and the longer Wikipedia article on the History of AI, with dates and titles differing a little for increased clarity.
The very first AI summer: unreasonable vitality, 1948-1966
Success at early attempts in AI happened in three primary locations: artificial neural networks, understanding representation, and heuristic search, contributing to high expectations. This area summarizes Kautz’s reprise of early AI history.
Approaches motivated by human or animal cognition or habits
Cybernetic techniques tried to duplicate the feedback loops in between animals and their environments. A robotic turtle, with sensors, motors for driving and steering, and 7 vacuum tubes for control, based on a preprogrammed neural net, was built as early as 1948. This work can be viewed as an early precursor to later operate in neural networks, reinforcement knowing, and situated robotics. [20]
An important early symbolic AI program was the Logic theorist, composed by Allen Newell, Herbert Simon and Cliff Shaw in 1955-56, as it had the ability to show 38 primary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later generalized this work to produce a domain-independent problem solver, GPS (General Problem Solver). GPS solved issues represented with formal operators via state-space search using means-ends analysis. [21]
During the 1960s, symbolic techniques achieved great success at mimicing smart habits in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was concentrated in four organizations in the 1960s: Carnegie Mellon University, Stanford, MIT and (later on) University of Edinburgh. Every one established its own design of research. Earlier techniques based upon cybernetics or synthetic neural networks were deserted or pressed into the background.
Herbert Simon and Allen Newell studied human problem-solving skills and tried to formalize them, and their work laid the foundations of the field of expert system, along with cognitive science, operations research and management science. Their research study team used the outcomes of psychological experiments to develop programs that simulated the techniques that individuals utilized to resolve issues. [22] [23] This custom, centered at Carnegie Mellon University would ultimately culminate in the development of the Soar architecture in the center 1980s. [24] [25]
Heuristic search
In addition to the extremely specialized domain-specific type of knowledge that we will see later on used in specialist systems, early symbolic AI researchers found another more basic application of understanding. These were called heuristics, guidelines that assist a search in promising directions: „How can non-enumerative search be practical when the underlying problem is significantly tough? The approach promoted by Simon and Newell is to utilize heuristics: fast algorithms that might fail on some inputs or output suboptimal services.“ [26] Another crucial advance was to find a method to apply these heuristics that guarantees a solution will be found, if there is one, not withstanding the periodic fallibility of heuristics: „The A * algorithm provided a basic frame for complete and optimum heuristically directed search. A * is used as a subroutine within virtually every AI algorithm today but is still no magic bullet; its warranty of efficiency is purchased the expense of worst-case rapid time. [26]
Early deal with understanding representation and thinking
Early work covered both applications of formal thinking stressing first-order logic, along with attempts to handle common-sense thinking in a less formal way.
Modeling formal thinking with logic: the „neats“
Unlike Simon and Newell, John McCarthy felt that makers did not need to simulate the exact mechanisms of human thought, but could rather look for the essence of abstract thinking and problem-solving with reasoning, [27] regardless of whether people utilized the exact same algorithms. [a] His lab at Stanford (SAIL) concentrated on utilizing official logic to solve a variety of issues, including knowledge representation, planning and learning. [31] Logic was likewise the focus of the work at the University of Edinburgh and elsewhere in Europe which resulted in the advancement of the programs language Prolog and the science of logic programs. [32] [33]
Modeling implicit sensible knowledge with frames and scripts: the „scruffies“
Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] found that solving tough problems in vision and natural language processing needed advertisement hoc solutions-they argued that no easy and general principle (like logic) would record all the aspects of intelligent behavior. Roger Schank described their „anti-logic“ methods as „shabby“ (instead of the „neat“ paradigms at CMU and Stanford). [36] [37] Commonsense understanding bases (such as Doug Lenat’s Cyc) are an example of „shabby“ AI, because they should be constructed by hand, one complicated idea at a time. [38] [39] [40]
The first AI winter season: crushed dreams, 1967-1977
The first AI winter season was a shock:
During the very first AI summer, lots of people believed that machine intelligence might be achieved in just a couple of years. The Defense Advance Research Projects Agency (DARPA) introduced programs to support AI research to utilize AI to resolve issues of nationwide security; in particular, to automate the translation of Russian to English for intelligence operations and to produce self-governing tanks for the battlefield. Researchers had begun to understand that accomplishing AI was going to be much harder than was supposed a decade previously, however a combination of hubris and disingenuousness led numerous university and think-tank scientists to accept funding with promises of deliverables that they need to have understood they might not fulfill. By the mid-1960s neither beneficial natural language translation systems nor self-governing tanks had actually been created, and a remarkable reaction embeded in. New DARPA management canceled existing AI financing programs.
Outside of the United States, the most fertile ground for AI research study was the United Kingdom. The AI winter season in the United Kingdom was spurred on not so much by disappointed military leaders as by rival academics who viewed AI scientists as charlatans and a drain on research study funding. A teacher of used mathematics, Sir James Lighthill, was commissioned by Parliament to assess the state of AI research in the country. The report stated that all of the problems being worked on in AI would be much better handled by researchers from other disciplines-such as applied mathematics. The report also declared that AI successes on toy issues could never scale to real-world applications due to combinatorial surge. [41]
The 2nd AI summer: knowledge is power, 1978-1987
Knowledge-based systems
As constraints with weak, domain-independent approaches ended up being more and more apparent, [42] researchers from all three customs began to build understanding into AI applications. [43] [7] The understanding revolution was driven by the awareness that knowledge underlies high-performance, domain-specific AI applications.
Edward Feigenbaum said:
– „In the understanding lies the power.“ [44]
to explain that high performance in a particular domain needs both general and highly domain-specific knowledge. Ed Feigenbaum and Doug Lenat called this The Knowledge Principle:
( 1) The Knowledge Principle: if a program is to carry out a complicated job well, it needs to understand an excellent offer about the world in which it runs.
( 2) A possible extension of that principle, called the Breadth Hypothesis: there are two additional abilities necessary for intelligent behavior in unanticipated situations: drawing on significantly basic understanding, and analogizing to particular however far-flung understanding. [45]
Success with specialist systems
This „knowledge revolution“ led to the advancement and implementation of expert systems (presented by Edward Feigenbaum), the first commercially successful type of AI software application. [46] [47] [48]
Key expert systems were:
DENDRAL, which found the structure of natural molecules from their chemical formula and mass spectrometer readings.
MYCIN, which identified bacteremia – and suggested further lab tests, when essential – by analyzing lab outcomes, client history, and physician observations. „With about 450 rules, MYCIN was able to perform in addition to some experts, and considerably much better than junior doctors.“ [49] INTERNIST and CADUCEUS which took on internal medication diagnosis. Internist tried to record the knowledge of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS could ultimately diagnose approximately 1000 various diseases.
– GUIDON, which revealed how a knowledge base constructed for specialist issue fixing could be repurposed for mentor. [50] XCON, to configure VAX computers, a then tiresome process that could use up to 90 days. XCON decreased the time to about 90 minutes. [9]
DENDRAL is thought about the very first expert system that depend on knowledge-intensive analytical. It is explained listed below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:
One of individuals at Stanford thinking about computer-based designs of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genetics. When I informed him I desired an induction „sandbox“, he said, „I have simply the one for you.“ His lab was doing mass spectrometry of amino acids. The question was: how do you go from taking a look at the spectrum of an amino acid to the chemical structure of the amino acid? That’s how we began the DENDRAL Project: I was good at heuristic search techniques, and he had an algorithm that was good at generating the chemical problem area.
We did not have a grandiose vision. We worked bottom up. Our chemist was Carl Djerassi, innovator of the chemical behind the contraceptive pill, and likewise among the world’s most respected mass spectrometrists. Carl and his postdocs were first-rate professionals in mass spectrometry. We started to add to their knowledge, inventing understanding of engineering as we went along. These experiments amounted to titrating DENDRAL a growing number of understanding. The more you did that, the smarter the program ended up being. We had great results.
The generalization was: in the understanding lies the power. That was the huge concept. In my career that is the big, „Ah ha!,“ and it wasn’t the way AI was being done formerly. Sounds simple, however it’s probably AI’s most effective generalization. [51]
The other specialist systems mentioned above came after DENDRAL. MYCIN exhibits the traditional expert system architecture of a knowledge-base of rules combined to a symbolic reasoning system, including making use of certainty elements to deal with unpredictability. GUIDON demonstrates how an explicit understanding base can be repurposed for a second application, tutoring, and is an example of a smart tutoring system, a specific sort of knowledge-based application. Clancey showed that it was not enough just to utilize MYCIN’s guidelines for instruction, however that he also required to add rules for discussion management and student modeling. [50] XCON is substantial due to the fact that of the millions of dollars it conserved DEC, which set off the professional system boom where most all major corporations in the US had expert systems groups, to capture corporate proficiency, preserve it, and automate it:
By 1988, DEC’s AI group had 40 specialist systems deployed, with more on the way. DuPont had 100 in use and 500 in advancement. Nearly every significant U.S. corporation had its own Al group and was either utilizing or investigating specialist systems. [49]
Chess expert knowledge was encoded in Deep Blue. In 1996, this allowed IBM’s Deep Blue, with the aid of symbolic AI, to win in a game of chess versus the world champion at that time, Garry Kasparov. [52]
Architecture of knowledge-based and expert systems
A key element of the system architecture for all specialist systems is the knowledge base, which stores facts and rules for analytical. [53] The easiest approach for a professional system knowledge base is simply a collection or network of production rules. Production rules link signs in a relationship similar to an If-Then declaration. The expert system processes the rules to make deductions and to determine what additional details it needs, i.e. what questions to ask, utilizing human-readable symbols. For instance, OPS5, CLIPS and their followers Jess and Drools run in this style.
Expert systems can run in either a forward chaining – from evidence to conclusions – or backwards chaining – from goals to required data and prerequisites – manner. More sophisticated knowledge-based systems, such as Soar can also perform meta-level thinking, that is thinking about their own thinking in regards to choosing how to solve issues and keeping an eye on the success of problem-solving methods.
Blackboard systems are a 2nd sort of knowledge-based or professional system architecture. They design a neighborhood of specialists incrementally contributing, where they can, to fix a problem. The problem is represented in multiple levels of abstraction or alternate views. The experts (understanding sources) volunteer their services whenever they acknowledge they can contribute. Potential problem-solving actions are represented on a program that is updated as the issue situation changes. A controller chooses how beneficial each contribution is, and who ought to make the next analytical action. One example, the BB1 chalkboard architecture [54] was initially inspired by studies of how humans plan to perform multiple tasks in a journey. [55] An innovation of BB1 was to use the very same chalkboard design to solving its control problem, i.e., its controller carried out meta-level thinking with knowledge sources that kept track of how well a strategy or the problem-solving was proceeding and could switch from one technique to another as conditions – such as goals or times – changed. BB1 has been used in multiple domains: building and construction site planning, smart tutoring systems, and real-time patient tracking.
The 2nd AI winter, 1988-1993
At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were selling LISP devices specifically targeted to speed up the advancement of AI applications and research study. In addition, several expert system business, such as Teknowledge and Inference Corporation, were selling professional system shells, training, and seeking advice from to corporations.
Unfortunately, the AI boom did not last and Kautz best describes the second AI winter that followed:
Many factors can be offered for the arrival of the second AI winter. The hardware companies stopped working when much more economical basic Unix workstations from Sun together with excellent compilers for LISP and Prolog came onto the market. Many business implementations of expert systems were discontinued when they showed too pricey to keep. Medical professional systems never ever captured on for numerous factors: the trouble in keeping them approximately date; the difficulty for physician to discover how to use a bewildering range of different specialist systems for different medical conditions; and maybe most crucially, the reluctance of physicians to trust a computer-made diagnosis over their gut instinct, even for particular domains where the specialist systems might outshine a typical medical professional. Equity capital cash deserted AI virtually overnight. The world AI conference IJCAI hosted a massive and extravagant trade show and thousands of nonacademic attendees in 1987 in Vancouver; the main AI conference the list below year, AAAI 1988 in St. Paul, was a small and strictly scholastic affair. [9]
Including more rigorous structures, 1993-2011
Uncertain thinking
Both statistical methods and extensions to reasoning were attempted.
One analytical method, concealed Markov models, had currently been promoted in the 1980s for speech acknowledgment work. [11] Subsequently, in 1988, Judea Pearl promoted using Bayesian Networks as a noise but efficient method of dealing with uncertain thinking with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian techniques were applied successfully in professional systems. [57] Even later on, in the 1990s, analytical relational learning, a technique that integrates possibility with logical solutions, enabled probability to be combined with first-order reasoning, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.
Other, non-probabilistic extensions to first-order logic to support were likewise attempted. For example, non-monotonic thinking might be used with fact upkeep systems. A fact maintenance system tracked presumptions and validations for all inferences. It permitted reasonings to be withdrawn when assumptions were found out to be incorrect or a contradiction was obtained. Explanations might be attended to a reasoning by discussing which guidelines were applied to create it and then continuing through underlying reasonings and rules all the way back to root assumptions. [58] Lofti Zadeh had actually presented a various kind of extension to manage the representation of vagueness. For instance, in deciding how „heavy“ or „tall“ a man is, there is often no clear „yes“ or „no“ response, and a predicate for heavy or tall would instead return worths in between 0 and 1. Those worths represented to what degree the predicates held true. His fuzzy logic even more provided a method for propagating combinations of these worths through rational formulas. [59]
Machine learning
Symbolic device discovering techniques were examined to attend to the knowledge acquisition bottleneck. Among the earliest is Meta-DENDRAL. Meta-DENDRAL used a generate-and-test strategy to create possible rule hypotheses to check against spectra. Domain and job understanding reduced the number of prospects tested to a manageable size. Feigenbaum described Meta-DENDRAL as
… the conclusion of my imagine the early to mid-1960s relating to theory development. The conception was that you had a problem solver like DENDRAL that took some inputs and produced an output. In doing so, it utilized layers of understanding to steer and prune the search. That knowledge got in there since we talked to people. But how did individuals get the understanding? By taking a look at thousands of spectra. So we desired a program that would look at thousands of spectra and infer the understanding of mass spectrometry that DENDRAL could utilize to resolve private hypothesis development issues. We did it. We were even able to release new understanding of mass spectrometry in the Journal of the American Chemical Society, offering credit only in a footnote that a program, Meta-DENDRAL, in fact did it. We were able to do something that had been a dream: to have a computer program developed a new and publishable piece of science. [51]
In contrast to the knowledge-intensive technique of Meta-DENDRAL, Ross Quinlan invented a domain-independent approach to statistical category, decision tree learning, starting initially with ID3 [60] and then later extending its capabilities to C4.5. [61] The choice trees created are glass box, interpretable classifiers, with human-interpretable classification guidelines.
Advances were made in comprehending maker knowing theory, too. Tom Mitchell introduced version area learning which describes learning as an explore a space of hypotheses, with upper, more basic, and lower, more particular, limits encompassing all practical hypotheses constant with the examples seen up until now. [62] More formally, Valiant introduced Probably Approximately Correct Learning (PAC Learning), a structure for the mathematical analysis of artificial intelligence. [63]
Symbolic maker learning incorporated more than finding out by example. E.g., John Anderson provided a cognitive model of human knowing where ability practice leads to a collection of guidelines from a declarative format to a procedural format with his ACT-R cognitive architecture. For example, a trainee may discover to apply „Supplementary angles are two angles whose procedures sum 180 degrees“ as several various procedural rules. E.g., one guideline might say that if X and Y are extra and you understand X, then Y will be 180 – X. He called his method „knowledge collection“. ACT-R has been used successfully to model elements of human cognition, such as finding out and retention. ACT-R is likewise used in smart tutoring systems, called cognitive tutors, to effectively teach geometry, computer system programming, and algebra to school children. [64]
Inductive logic programming was another approach to discovering that enabled reasoning programs to be synthesized from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) could manufacture Prolog programs from examples. [65] John R. Koza applied genetic algorithms to program synthesis to develop genetic programs, which he utilized to manufacture LISP programs. Finally, Zohar Manna and Richard Waldinger offered a more general approach to program synthesis that synthesizes a functional program in the course of proving its specs to be right. [66]
As an option to reasoning, Roger Schank presented case-based thinking (CBR). The CBR technique outlined in his book, Dynamic Memory, [67] focuses initially on keeping in mind crucial analytical cases for future usage and generalizing them where appropriate. When faced with a new problem, CBR obtains the most similar previous case and adapts it to the specifics of the existing issue. [68] Another alternative to reasoning, hereditary algorithms and hereditary shows are based upon an evolutionary design of knowing, where sets of rules are encoded into populations, the rules govern the habits of individuals, and choice of the fittest prunes out sets of unsuitable rules over numerous generations. [69]
Symbolic maker learning was applied to finding out principles, rules, heuristics, and analytical. Approaches, aside from those above, include:
1. Learning from instruction or advice-i.e., taking human guideline, impersonated recommendations, and determining how to operationalize it in particular situations. For example, in a game of Hearts, finding out precisely how to play a hand to „avoid taking points.“ [70] 2. Learning from exemplars-improving efficiency by accepting subject-matter specialist (SME) feedback during training. When analytical fails, querying the expert to either learn a new exemplar for analytical or to discover a new explanation regarding precisely why one prototype is more relevant than another. For instance, the program Protos found out to detect ringing in the ears cases by interacting with an audiologist. [71] 3. Learning by analogy-constructing issue options based on comparable problems seen in the past, and then modifying their options to fit a brand-new circumstance or domain. [72] [73] 4. Apprentice learning systems-learning unique solutions to issues by observing human analytical. Domain knowledge explains why unique options are appropriate and how the option can be generalized. LEAP found out how to create VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., developing tasks to bring out experiments and after that gaining from the results. Doug Lenat’s Eurisko, for instance, learned heuristics to beat human players at the Traveller role-playing game for 2 years in a row. [75] 6. Learning macro-operators-i.e., browsing for useful macro-operators to be learned from sequences of basic analytical actions. Good macro-operators simplify analytical by permitting issues to be fixed at a more abstract level. [76]
Deep learning and neuro-symbolic AI 2011-now
With the rise of deep knowing, the symbolic AI method has actually been compared to deep learning as complementary „… with parallels having been drawn sometimes by AI researchers between Kahneman’s research study on human reasoning and decision making – reflected in his book Thinking, Fast and Slow – and the so-called „AI systems 1 and 2″, which would in concept be modelled by deep learning and symbolic reasoning, respectively.“ In this view, symbolic reasoning is more apt for deliberative reasoning, preparation, and description while deep learning is more apt for fast pattern recognition in affective applications with noisy information. [17] [18]
Neuro-symbolic AI: incorporating neural and symbolic approaches
Neuro-symbolic AI attempts to integrate neural and symbolic architectures in a way that addresses strengths and weaknesses of each, in a complementary fashion, in order to support robust AI efficient in reasoning, learning, and cognitive modeling. As argued by Valiant [77] and many others, [78] the reliable building and construction of rich computational cognitive designs requires the mix of sound symbolic reasoning and effective (maker) learning designs. Gary Marcus, similarly, argues that: „We can not construct rich cognitive models in an appropriate, automatic method without the set of three of hybrid architecture, rich prior knowledge, and sophisticated techniques for thinking.“, [79] and in particular: „To construct a robust, knowledge-driven technique to AI we must have the equipment of symbol-manipulation in our toolkit. Too much of useful understanding is abstract to make do without tools that represent and manipulate abstraction, and to date, the only machinery that we understand of that can control such abstract knowledge reliably is the apparatus of sign control. “ [80]
Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have also argued for a synthesis. Their arguments are based on a requirement to deal with the two type of believing gone over in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman describes human thinking as having 2 components, System 1 and System 2. System 1 is quick, automated, user-friendly and unconscious. System 2 is slower, step-by-step, and specific. System 1 is the kind utilized for pattern recognition while System 2 is far much better matched for preparation, reduction, and deliberative thinking. In this view, deep knowing finest models the very first type of believing while symbolic thinking best models the 2nd kind and both are required.
Garcez and Lamb explain research study in this area as being ongoing for a minimum of the past twenty years, [83] dating from their 2002 book on neurosymbolic knowing systems. [84] A series of workshops on neuro-symbolic thinking has been held every year given that 2005, see http://www.neural-symbolic.org/ for details.
In their 2015 paper, Neural-Symbolic Learning and Reasoning: Contributions and Challenges, Garcez et al. argue that:
The integration of the symbolic and connectionist paradigms of AI has actually been pursued by a relatively small research study community over the last 20 years and has actually yielded several substantial outcomes. Over the last decade, neural symbolic systems have actually been shown capable of conquering the so-called propositional fixation of neural networks, as McCarthy (1988) put it in response to Smolensky (1988 ); see also (Hinton, 1990). Neural networks were revealed efficient in representing modal and temporal reasonings (d’Avila Garcez and Lamb, 2006) and fragments of first-order logic (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been used to a number of problems in the areas of bioinformatics, control engineering, software application confirmation and adaptation, visual intelligence, ontology learning, and computer system video games. [78]
Approaches for integration are varied. Henry Kautz’s taxonomy of neuro-symbolic architectures, along with some examples, follows:
– Symbolic Neural symbolic-is the present technique of lots of neural designs in natural language processing, where words or subword tokens are both the supreme input and output of big language models. Examples include BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exhibited by AlphaGo, where symbolic techniques are used to call neural methods. In this case the symbolic method is Monte Carlo tree search and the neural techniques find out how to evaluate game positions.
– Neural|Symbolic-uses a neural architecture to analyze affective data as signs and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic thinking to produce or label training information that is subsequently discovered by a deep knowing model, e.g., to train a neural model for symbolic calculation by using a Macsyma-like symbolic mathematics system to create or identify examples.
– Neural _ Symbolic -uses a neural internet that is produced from symbolic guidelines. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR proof tree generated from knowledge base rules and terms. Logic Tensor Networks [86] also fall under this classification.
– Neural [Symbolic] -enables a neural model to straight call a symbolic reasoning engine, e.g., to carry out an action or evaluate a state.
Many crucial research questions remain, such as:
– What is the finest method to integrate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and extracted from them?
– How should sensible understanding be discovered and reasoned about?
– How can abstract understanding that is hard to encode logically be handled?
Techniques and contributions
This area provides an introduction of strategies and contributions in an overall context leading to many other, more detailed posts in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered previously in the history section.
AI programming languages
The key AI shows language in the US during the last symbolic AI boom duration was LISP. LISP is the second oldest shows language after FORTRAN and was developed in 1958 by John McCarthy. LISP supplied the first read-eval-print loop to support fast program advancement. Compiled functions might be freely mixed with analyzed functions. Program tracing, stepping, and breakpoints were likewise provided, in addition to the capability to alter worths or functions and continue from breakpoints or mistakes. It had the first self-hosting compiler, implying that the compiler itself was initially written in LISP and then ran interpretively to compile the compiler code.
Other key developments originated by LISP that have infected other programs languages consist of:
Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals
Programs were themselves data structures that other programs could run on, enabling the simple meaning of higher-level languages.
In contrast to the US, in Europe the key AI shows language throughout that exact same period was Prolog. Prolog supplied an integrated shop of facts and provisions that might be queried by a read-eval-print loop. The store could serve as a knowledge base and the clauses might serve as guidelines or a limited form of logic. As a subset of first-order reasoning Prolog was based on Horn provisions with a closed-world assumption-any realities not known were considered false-and an unique name presumption for primitive terms-e.g., the identifier barack_obama was considered to refer to exactly one item. Backtracking and marriage are built-in to Prolog.
Alain Colmerauer and Philippe Roussel are credited as the innovators of Prolog. Prolog is a kind of logic programming, which was created by Robert Kowalski. Its history was also influenced by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of methods. For more information see the area on the origins of Prolog in the PLANNER article.
Prolog is also a kind of declarative programming. The reasoning stipulations that explain programs are directly interpreted to run the programs specified. No specific series of actions is required, as is the case with necessary programming languages.
Japan promoted Prolog for its Fifth Generation Project, intending to develop special hardware for high performance. Similarly, LISP machines were developed to run LISP, however as the 2nd AI boom turned to bust these companies might not take on new workstations that could now run LISP or Prolog natively at equivalent speeds. See the history area for more detail.
Smalltalk was another influential AI programming language. For instance, it presented metaclasses and, along with Flavors and CommonLoops, affected the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the present standard Lisp dialect. CLOS is a Lisp-based object-oriented system that allows numerous inheritance, in addition to incremental extensions to both classes and metaclasses, thus providing a run-time meta-object procedure. [88]
For other AI shows languages see this list of programs languages for artificial intelligence. Currently, Python, a multi-paradigm shows language, is the most popular programs language, partially due to its extensive package library that supports information science, natural language processing, and deep knowing. Python consists of a read-eval-print loop, functional components such as higher-order functions, and object-oriented programs that includes metaclasses.
Search
Search arises in numerous sort of problem solving, including preparation, constraint complete satisfaction, and playing video games such as checkers, chess, and go. The finest known AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven clause learning, and the DPLL algorithm. For adversarial search when playing video games, alpha-beta pruning, branch and bound, and minimax were early contributions.
Knowledge representation and reasoning
Multiple different approaches to represent knowledge and then reason with those representations have been examined. Below is a fast summary of approaches to knowledge representation and automated thinking.
Knowledge representation
Semantic networks, conceptual charts, frames, and logic are all methods to modeling understanding such as domain knowledge, problem-solving understanding, and the semantic significance of language. Ontologies model key principles and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be used for any domain while WordNet is a lexical resource that can also be deemed an ontology. YAGO incorporates WordNet as part of its ontology, to line up truths drawn out from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology currently being utilized.
Description reasoning is a reasoning for automated category of ontologies and for finding inconsistent classification data. OWL is a language used to represent ontologies with description logic. Protégé is an ontology editor that can check out in OWL ontologies and after that check consistency with deductive classifiers such as such as HermiT. [89]
First-order logic is more basic than description reasoning. The automated theorem provers discussed listed below can show theorems in first-order reasoning. Horn provision reasoning is more restricted than first-order logic and is used in reasoning programs languages such as Prolog. Extensions to first-order reasoning consist of temporal reasoning, to deal with time; epistemic logic, to reason about representative understanding; modal reasoning, to handle possibility and requirement; and probabilistic reasonings to handle reasoning and possibility together.
Automatic theorem showing
Examples of automated theorem provers for first-order logic are:
Prover9.
ACL2.
Vampire.
Prover9 can be used in conjunction with the Mace4 design checker. ACL2 is a theorem prover that can manage proofs by induction and is a descendant of the Boyer-Moore Theorem Prover, also referred to as Nqthm.
Reasoning in knowledge-based systems
Knowledge-based systems have an explicit understanding base, normally of rules, to improve reusability across domains by separating procedural code and domain knowledge. A separate inference engine procedures rules and adds, deletes, or customizes an understanding store.
Forward chaining reasoning engines are the most typical, and are seen in CLIPS and OPS5. Backward chaining occurs in Prolog, where a more minimal logical representation is utilized, Horn Clauses. Pattern-matching, specifically unification, is utilized in Prolog.
A more versatile sort of analytical takes place when reasoning about what to do next occurs, instead of simply picking among the available actions. This type of meta-level reasoning is used in Soar and in the BB1 blackboard architecture.
Cognitive architectures such as ACT-R might have extra abilities, such as the ability to compile frequently used understanding into higher-level portions.
Commonsense reasoning
Marvin Minsky first proposed frames as a method of interpreting common visual situations, such as an office, and Roger Schank extended this concept to scripts for typical regimens, such as dining out. Cyc has tried to record useful sensible understanding and has „micro-theories“ to handle specific type of domain-specific reasoning.
Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] approximates human thinking about ignorant physics, such as what takes place when we warm a liquid in a pot on the stove. We expect it to heat and perhaps boil over, despite the fact that we may not understand its temperature, its boiling point, or other details, such as climatic pressure.
Similarly, Allen’s temporal period algebra is a simplification of reasoning about time and Region Connection Calculus is a simplification of thinking about spatial relationships. Both can be solved with restriction solvers.
Constraints and constraint-based reasoning
Constraint solvers carry out a more restricted type of reasoning than first-order reasoning. They can streamline sets of spatiotemporal restraints, such as those for RCC or Temporal Algebra, along with resolving other sort of puzzle problems, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint reasoning programs can be used to fix scheduling issues, for example with restraint managing guidelines (CHR).
Automated preparation
The General Problem Solver (GPS) cast planning as analytical used means-ends analysis to produce plans. STRIPS took a different technique, seeing preparation as theorem proving. Graphplan takes a least-commitment method to preparation, instead of sequentially picking actions from a preliminary state, working forwards, or an objective state if working in reverse. Satplan is an approach to planning where a planning issue is decreased to a Boolean satisfiability problem.
Natural language processing
Natural language processing focuses on treating language as information to carry out jobs such as identifying topics without necessarily comprehending the desired meaning. Natural language understanding, in contrast, constructs a meaning representation and uses that for more processing, such as answering questions.
Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all elements of natural language processing long dealt with by symbolic AI, but because enhanced by deep learning methods. In symbolic AI, discourse representation theory and first-order reasoning have been utilized to represent sentence meanings. Latent semantic analysis (LSA) and explicit semantic analysis likewise offered vector representations of documents. In the latter case, vector components are interpretable as principles called by Wikipedia posts.
New deep knowing techniques based upon Transformer models have now eclipsed these earlier symbolic AI techniques and attained state-of-the-art efficiency in natural language processing. However, Transformer models are opaque and do not yet produce human-interpretable semantic representations for sentences and files. Instead, they produce task-specific vectors where the meaning of the vector components is opaque.
Agents and multi-agent systems
Agents are self-governing systems embedded in an environment they view and act upon in some sense. Russell and Norvig’s standard book on expert system is arranged to reflect agent architectures of increasing sophistication. [91] The sophistication of agents differs from basic reactive agents, to those with a model of the world and automated planning abilities, possibly a BDI representative, i.e., one with beliefs, desires, and objectives – or additionally a reinforcement discovering model found out with time to pick actions – as much as a mix of alternative architectures, such as a neuro-symbolic architecture [87] that includes deep knowing for perception. [92]
On the other hand, a multi-agent system includes numerous representatives that communicate amongst themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language (KQML). The agents require not all have the very same internal architecture. Advantages of multi-agent systems include the capability to divide work amongst the agents and to increase fault tolerance when representatives are lost. Research problems consist of how representatives reach agreement, dispersed problem fixing, multi-agent knowing, multi-agent planning, and distributed restriction optimization.
Controversies emerged from early in symbolic AI, both within the field-e.g., in between logicists (the pro-logic „neats“) and non-logicists (the anti-logic „scruffies“)- and in between those who welcomed AI but turned down symbolic approaches-primarily connectionists-and those outside the field. Critiques from beyond the field were primarily from thinkers, on intellectual grounds, but likewise from financing companies, specifically throughout the two AI winters.
The Frame Problem: knowledge representation obstacles for first-order logic
Limitations were discovered in using basic first-order logic to factor about dynamic domains. Problems were found both with regards to enumerating the preconditions for an action to succeed and in supplying axioms for what did not change after an action was performed.
McCarthy and Hayes presented the Frame Problem in 1969 in the paper, „Some Philosophical Problems from the Standpoint of Expert System.“ [93] An easy example occurs in „proving that one person could enter discussion with another“, as an axiom asserting „if an individual has a telephone he still has it after looking up a number in the telephone book“ would be needed for the reduction to succeed. Similar axioms would be needed for other domain actions to define what did not change.
A similar issue, called the Qualification Problem, takes place in trying to mention the preconditions for an action to prosper. A boundless variety of pathological conditions can be thought of, e.g., a banana in a tailpipe could avoid an automobile from operating properly.
McCarthy’s approach to fix the frame issue was circumscription, a sort of non-monotonic reasoning where reductions might be made from actions that need just define what would alter while not having to explicitly specify whatever that would not alter. Other non-monotonic reasonings supplied truth upkeep systems that revised beliefs leading to contradictions.
Other ways of managing more open-ended domains consisted of probabilistic thinking systems and device learning to find out brand-new ideas and guidelines. McCarthy’s Advice Taker can be deemed an inspiration here, as it might include new knowledge supplied by a human in the kind of assertions or guidelines. For example, speculative symbolic maker finding out systems explored the capability to take top-level natural language suggestions and to translate it into domain-specific actionable rules.
Similar to the problems in handling vibrant domains, common-sense reasoning is likewise tough to capture in formal reasoning. Examples of common-sense reasoning include implicit thinking about how individuals think or general knowledge of daily events, things, and living creatures. This type of knowledge is taken for approved and not deemed noteworthy. Common-sense thinking is an open location of research study and challenging both for symbolic systems (e.g., Cyc has tried to capture essential parts of this knowledge over more than a years) and neural systems (e.g., self-driving cars that do not understand not to drive into cones or not to hit pedestrians walking a bike).
McCarthy saw his Advice Taker as having common-sense, but his definition of common-sense was various than the one above. [94] He defined a program as having typical sense „if it automatically deduces for itself an adequately broad class of instant effects of anything it is told and what it already understands. „
Connectionist AI: philosophical obstacles and sociological disputes
Connectionist approaches include earlier work on neural networks, [95] such as perceptrons; operate in the mid to late 80s, such as Danny Hillis’s Connection Machine and Yann LeCun’s advances in convolutional neural networks; to today’s advanced approaches, such as Transformers, GANs, and other operate in deep learning.
Three philosophical positions [96] have been laid out among connectionists:
1. Implementationism-where connectionist architectures implement the capabilities for symbolic processing,
2. Radical connectionism-where symbolic processing is declined absolutely, and connectionist architectures underlie intelligence and are fully sufficient to explain it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are considered as complementary and both are needed for intelligence
Olazaran, in his sociological history of the debates within the neural network community, explained the moderate connectionism consider as basically compatible with current research in neuro-symbolic hybrids:
The third and last position I would like to examine here is what I call the moderate connectionist view, a more eclectic view of the present debate in between connectionism and symbolic AI. Among the scientists who has actually elaborated this position most explicitly is Andy Clark, a thinker from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark protected hybrid (partially symbolic, partially connectionist) systems. He claimed that (a minimum of) two type of theories are required in order to study and model cognition. On the one hand, for some information-processing tasks (such as pattern acknowledgment) connectionism has benefits over symbolic models. But on the other hand, for other cognitive processes (such as serial, deductive thinking, and generative symbol manipulation processes) the symbolic paradigm uses appropriate designs, and not just „approximations“ (contrary to what radical connectionists would declare). [97]
Gary Marcus has actually declared that the animus in the deep learning community versus symbolic approaches now might be more sociological than philosophical:
To believe that we can simply desert symbol-manipulation is to suspend shock.
And yet, for the a lot of part, that’s how most existing AI earnings. Hinton and numerous others have tried hard to banish signs altogether. The deep knowing hope-seemingly grounded not a lot in science, but in a sort of historical grudge-is that smart habits will emerge simply from the confluence of massive data and deep learning. Where classical computer systems and software solve tasks by defining sets of symbol-manipulating rules committed to particular jobs, such as modifying a line in a word processor or carrying out a calculation in a spreadsheet, neural networks generally attempt to fix tasks by statistical approximation and gaining from examples.
According to Marcus, Geoffrey Hinton and his coworkers have been emphatically „anti-symbolic“:
When deep learning reemerged in 2012, it was with a kind of take-no-prisoners mindset that has defined the majority of the last decade. By 2015, his hostility towards all things symbols had fully crystallized. He gave a talk at an AI workshop at Stanford comparing signs to aether, one of science’s biggest errors.
…
Since then, his anti-symbolic project has actually only increased in intensity. In 2016, Yann LeCun, Bengio, and Hinton wrote a manifesto for deep knowing in one of science’s most essential journals, Nature. It closed with a direct attack on symbol control, calling not for reconciliation however for outright replacement. Later, Hinton informed a gathering of European Union leaders that investing any additional cash in symbol-manipulating approaches was „a substantial error,“ comparing it to buying internal combustion engines in the era of electric cars and trucks. [98]
Part of these disputes might be due to unclear terms:
Turing award winner Judea Pearl offers a review of machine learning which, sadly, conflates the terms maker learning and deep knowing. Similarly, when Geoffrey Hinton refers to symbolic AI, the undertone of the term tends to be that of specialist systems dispossessed of any ability to find out. Making use of the terminology is in requirement of clarification. Machine learning is not confined to association guideline mining, c.f. the body of work on symbolic ML and relational knowing (the distinctions to deep knowing being the option of representation, localist sensible rather than dispersed, and the non-use of gradient-based learning algorithms). Equally, symbolic AI is not practically production rules written by hand. An appropriate definition of AI issues knowledge representation and reasoning, autonomous multi-agent systems, planning and argumentation, as well as knowing. [99]
Situated robotics: the world as a model
Another review of symbolic AI is the embodied cognition approach:
The embodied cognition method declares that it makes no sense to consider the brain independently: cognition takes location within a body, which is embedded in an environment. We require to study the system as a whole; the brain’s functioning exploits regularities in its environment, including the rest of its body. Under the embodied cognition approach, robotics, vision, and other sensing units become central, not peripheral. [100]
Rodney Brooks created behavior-based robotics, one method to embodied cognition. Nouvelle AI, another name for this method, is considered as an alternative to both symbolic AI and connectionist AI. His technique rejected representations, either symbolic or distributed, as not only unnecessary, however as damaging. Instead, he developed the subsumption architecture, a layered architecture for embodied agents. Each layer attains a different function and should work in the real world. For example, the first robotic he describes in Intelligence Without Representation, has 3 layers. The bottom layer interprets sonar sensors to prevent objects. The middle layer causes the robotic to roam around when there are no barriers. The top layer causes the robot to go to more distant locations for further expedition. Each layer can briefly prevent or reduce a lower-level layer. He slammed AI researchers for defining AI problems for their systems, when: „There is no tidy department between understanding (abstraction) and thinking in the real life.“ [101] He called his robotics „Creatures“ and each layer was „composed of a fixed-topology network of basic limited state makers.“ [102] In the Nouvelle AI technique, „First, it is critically important to test the Creatures we construct in the genuine world; i.e., in the same world that we human beings live in. It is disastrous to fall under the temptation of testing them in a simplified world first, even with the best intents of later transferring activity to an unsimplified world.“ [103] His emphasis on real-world screening remained in contrast to „Early work in AI focused on games, geometrical problems, symbolic algebra, theorem proving, and other formal systems“ [104] and making use of the blocks world in symbolic AI systems such as SHRDLU.
Current views
Each approach-symbolic, connectionist, and behavior-based-has advantages, however has been slammed by the other methods. Symbolic AI has been slammed as disembodied, accountable to the qualification issue, and bad in dealing with the perceptual problems where deep finding out excels. In turn, connectionist AI has been slammed as poorly matched for deliberative detailed issue resolving, incorporating understanding, and dealing with preparation. Finally, Nouvelle AI excels in reactive and real-world robotics domains however has been criticized for troubles in incorporating knowing and understanding.
Hybrid AIs integrating several of these approaches are presently considered as the path forward. [19] [81] [82] Russell and Norvig conclude that:
Overall, Dreyfus saw locations where AI did not have complete answers and said that Al is therefore impossible; we now see a number of these exact same areas going through continued research and advancement leading to increased capability, not impossibility. [100]
Expert system.
Automated preparation and scheduling
Automated theorem proving
Belief revision
Case-based thinking
Cognitive architecture
Cognitive science
Connectionism
Constraint programs
Deep learning
First-order logic
GOFAI
History of artificial intelligence
Inductive logic programs
Knowledge-based systems
Knowledge representation and reasoning
Logic programs
Machine knowing
Model checking
Model-based reasoning
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of expert system
Physical sign systems hypothesis
Semantic Web
Sequential pattern mining
Statistical relational learning
Symbolic mathematics
YAGO ontology
WordNet
Notes
^ McCarthy as soon as stated: „This is AI, so we don’t care if it’s mentally real“. [4] McCarthy repeated his position in 2006 at the AI@50 conference where he stated „Artificial intelligence is not, by meaning, simulation of human intelligence“. [28] Pamela McCorduck writes that there are „2 major branches of expert system: one aimed at producing intelligent habits regardless of how it was accomplished, and the other focused on modeling intelligent procedures found in nature, especially human ones.“, [29] Stuart Russell and Peter Norvig composed „Aeronautical engineering texts do not define the objective of their field as making ‚machines that fly so exactly like pigeons that they can fool even other pigeons.'“ [30] Citations
^ Garnelo, Marta; Shanahan, Murray (October 2019). „Reconciling deep knowing with symbolic expert system: representing items and relations“. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796.
^ Thomason, Richmond (February 27, 2024). „Logic-Based Expert System“. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). „Reconciling deep learning with symbolic artificial intelligence: representing items and relations“. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796. S2CID 72336067.
^ a b Kolata 1982.
^ Kautz 2022, pp. 107-109.
^ a b Russell & Norvig 2021, p. 19.
^ a b Russell & Norvig 2021, pp. 22-23.
^ a b Kautz 2022, pp. 109-110.
^ a b c Kautz 2022, p. 110.
^ Kautz 2022, pp. 110-111.
^ a b Russell & Norvig 2021, p. 25.
^ Kautz 2022, p. 111.
^ Kautz 2020, pp. 110-111.
^ Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (1986 ). „Learning representations by back-propagating errors“. Nature. 323 (6088 ): 533-536. Bibcode:1986 Natur.323..533 R. doi:10.1038/ 323533a0. ISSN 1476-4687. S2CID 205001834.
^ LeCun, Y.; Boser, B.; Denker, I.; Henderson, D.; Howard, R.; Hubbard, W.; Tackel, L. (1989 ). „Backpropagation Applied to Handwritten Zip Code Recognition“. Neural Computation. 1 (4 ): 541-551. doi:10.1162/ neco.1989.1.4.541. S2CID 41312633.
^ a b Marcus & Davis 2019.
^ a b Rossi, Francesca. „Thinking Fast and Slow in AI“. AAAI. Retrieved 5 July 2022.
^ a b Selman, Bart. „AAAI Presidential Address: The State of AI“. AAAI. Retrieved 5 July 2022.
^ a b c Kautz 2020.
^ Kautz 2022, p. 106.
^ Newell & Simon 1972.
^ & McCorduck 2004, pp. 139-179, 245-250, 322-323 (EPAM).
^ Crevier 1993, pp. 145-149.
^ McCorduck 2004, pp. 450-451.
^ Crevier 1993, pp. 258-263.
^ a b Kautz 2022, p. 108.
^ Russell & Norvig 2021, p. 9 (logicist AI), p. 19 (McCarthy’s work).
^ Maker 2006.
^ McCorduck 2004, pp. 100-101.
^ Russell & Norvig 2021, p. 2.
^ McCorduck 2004, pp. 251-259.
^ Crevier 1993, pp. 193-196.
^ Howe 1994.
^ McCorduck 2004, pp. 259-305.
^ Crevier 1993, pp. 83-102, 163-176.
^ McCorduck 2004, pp. 421-424, 486-489.
^ Crevier 1993, p. 168.
^ McCorduck 2004, p. 489.
^ Crevier 1993, pp. 239-243.
^ Russell & Norvig 2021, p. 316, 340.
^ Kautz 2022, p. 109.
^ Russell & Norvig 2021, p. 22.
^ McCorduck 2004, pp. 266-276, 298-300, 314, 421.
^ Shustek, Len (June 2010). „An interview with Ed Feigenbaum“. Communications of the ACM. 53 (6 ): 41-45. doi:10.1145/ 1743546.1743564. ISSN 0001-0782. S2CID 10239007. Retrieved 2022-07-14.
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^ Russell & Norvig 2021, pp. 22-24.
^ McCorduck 2004, pp. 327-335, 434-435.
^ Crevier 1993, pp. 145-62, 197-203.
^ a b Russell & Norvig 2021, p. 23.
^ a b Clancey 1987.
^ a b Shustek, Len (2010 ). „An interview with Ed Feigenbaum“. Communications of the ACM. 53 (6 ): 41-45. doi:10.1145/ 1743546.1743564. ISSN 0001-0782. S2CID 10239007. Retrieved 2022-08-05.
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